Regularized Deconvolution Method Research Articles

TU31. GENETIC CONTRIBUTION TO MAJOR DEPRESSIVE DISORDER HETEROGENEITY- FAMILY DESIGNS USING SWEDISH NATIONAL REGISTERS

The modeling of filtered chemical source terms in large-eddy simulation (LES) of turbulent reacting flows remains a challenge. Deconvolution methods are an attractive technique for representing these unclosed terms. With this technique, filtered scalars are reconstructed through deconvolution. The chemical source terms that are computed directly from the deconvolved scalars are filtered explicitly to represent the turbulence-chemistry interaction. However, the approximate deconvolution method (ADM), frequently employed for non-reacting flows, exhibits shortcomings for reacting scalars. This is because ADM does not ensure essential conservation and boundedness conditions. To address this issue, we propose a regularized deconvolution method (RDM) based on an optimization procedure. We conduct a priori and a posteriori analyses to examine RDM as a closure in LES. These investigations are performed in the context of explicit filtering. By showing that RDM is accurate and stable with respect to both the filter width and time, we conclude that the new deconvolution method shows promise in application to combustion LES.

Construction and application of a new biorthogonal wavelet basis for a quantitative analysis of GPR signals

Due to interference from coherent reflected waves and clutter, it is difficult for ground-penetrating radar (GPR) signals to enable exact quantitative analyses of tunnel void defects. According to GPR pulse features, a new wavelet basis (named the GPR3.3 wavelet) was constructed using an improved biorthogonal wavelet construction method. In addition, based on the wavelet transform modulus maximum method, a new quantitative analysis method for GPR signals that is based on the biorthogonal wavelet basis (the QAGBW method) is proposed. The QAGBW method based on the GPR3.3 wavelet was applied in a quantitative analysis of analog and measured signals. The results show that the GPR3.3 wavelet, which can identify the singularity of a GPR pulse, is an optimal wavelet basis for quantitative analysis. Furthermore, the QAGBW method based on the GPR3.3 wavelet has greater precision than the regularized deconvolution method for the quantitative analysis of the depth and vertical size of voids because it restrains the interference of coherent reflected waves and clutter.

A regularized deconvolution model for sub-grid dispersion in large eddy simulation of turbulent spray flames

In this study, a sub-grid scale (SGS) dispersion model is developed for simulating turbulent spray flames. This model employs a regularized deconvolution method (RDM) to determine the sub-grid fluctuations from the filtered velocity field. The model is examined in simulations of a turbulent counterflow n-dodecane spray flame, and benchmarked against a simplified Langevin model (SLM) and simulations without dispersion closure (NOM). To identify conditions that characterize the importance of the spray-flame coupling, a regime diagram is constructed from physical arguments and theoretical analysis by considering idealized conditions. Based on the proposed criteria, a range of operating conditions is considered, involving different Stokes- and Reynolds numbers as well as inert and reacting conditions. A priori DNS analyses are performed to compare the probability density functions (PDFs) of slip velocities. Significant discrepancies between models are observed for smaller Stokes numbers. Subsequently, large eddy simulations (LES) are performed and effects of mesh resolution on statistical flow-field quantities are investigated. Qualitative and quantitative differences are observed for coarse-grid simulations with larger droplets, where a double-flame structure is predicted by DNS and LES-RDM, whereas a single flame is predicted by LES-NOM and LES-SLM. For the finer grid, all three LES models predict the double-flame structure. Turbulence modulation by the spray is expected to play a role in creating different flame topologies, and the RDM-based SGS-closure model is shown to provide improved predictions for the dispersed and carrier phases for a wider range of grid resolution.

Serum free cortisol measurement by LC-MSMS

Due to interference from coherent reflected waves and clutter, it is difficult for ground-penetrating radar (GPR) signals to enable exact quantitative analyses of tunnel void defects. According to GPR pulse features, a new wavelet basis (named the GPR3.3 wavelet) was constructed using an improved biorthogonal wavelet construction method. In addition, based on the wavelet transform modulus maximum method, a new quantitative analysis method for GPR signals that is based on the biorthogonal wavelet basis (the QAGBW method) is proposed. The QAGBW method based on the GPR3.3 wavelet was applied in a quantitative analysis of analog and measured signals. The results show that the GPR3.3 wavelet, which can identify the singularity of a GPR pulse, is an optimal wavelet basis for quantitative analysis. Furthermore, the QAGBW method based on the GPR3.3 wavelet has greater precision than the regularized deconvolution method for the quantitative analysis of the depth and vertical size of voids because it restrains the interference of coherent reflected waves and clutter.

A regularized deconvolution method for turbulent closure modeling in implicitly filtered large-eddy simulation

Turbulence closure models that are based on a regularized deconvolution method (RDM) are proposed for application to implicitly filtered Large-Eddy Simulation (LES). This method reconstructs sub-grid scale contributions by approximately recovering filtered quantities through an optimization approach. Physical principles on the boundedness and conservation of reconstructed quantities are preserved by constraining the optimization problem. To account for effects of scales that are not resolved in implicitly filtered LES, a reconstruction method is proposed that combines sub-grid projection and energy-similarity to enrich unresolved scales. With this method, closure models are developed for turbulent scalar fluxes and turbulence–chemistry interaction that are intrinsically consistent with the LES formulation. These closure models are evaluated in application to a piloted turbulent jet flame with inhomogeneous inlet composition, in which combustion is represented by a two-scalar manifold model. The RDM-formulation is benchmarked against simulations obtained using a presumed PDF method and eddy diffusivity closure model. In partially-premixed flame regions, it is observed that RDM improves the prediction of turbulent mixing, and faster grid convergence is observed for results with RDM compared to simulations employing a presumed PDF method.

Regularized Deconvolution Method for the Resolution Enhancement of a Dual-Frequency Polarized Scatterometer on WCOM

A dual-frequency polarized scatterometer (DPSCAT) is proposed for the Chinese Water Cycle Observation Mission (WCOM) to be launched around 2020. DPSCAT is used to measure the snow water equivalent (SWE) and the freeze/thaw state, which requires a measurement precision of 0.5 dB and a relatively higher spatial resolution (2–5 km) than the regular scatterometers (about 25 km). Therefore, the conventional range-gate dechirping along with the Doppler beam sharpening (DBS) technique is used by DPSCAT to achieve high range and azimuth resolution simultaneously. However, DBS cannot improve the azimuth resolution over the nadir swath; thus, a new data processing, namely regularized deconvolution method (RDM), is explored to address this problem. In this paper, a quantitative analysis model is developed for RDM in order to study two crucial issues, i.e., the spatial resolution (mainly for the nadir swath) and the accuracy/precision of the backscatter measurements after resolution enhancement. Normally, the measurement precision and spatial resolution cannot be improved simultaneously using RDM. The accuracy/precision degrades as the spatial resolution improves, and vice versa. Moreover, they both degrade as the measurement noise or uncertainty increases, which latter is usually defined as the normalized standard deviation of the measurements ( $K_p$ ). In case of SWE retrieval that requires a reconstructed measurement precision of 0.5 dB, the best spatial resolution resolved by RDM is 3 km for $K_p$ = 7%, 4 km for $K_p$ = 10%, and 5 km for $K_p$ = 12%.

Regularized deconvolution method for turbulent combustion modeling

The modeling of filtered chemical source terms in large-eddy simulation (LES) of turbulent reacting flows remains a challenge. Deconvolution methods are an attractive technique for representing these unclosed terms. With this technique, filtered scalars are reconstructed through deconvolution. The chemical source terms that are computed directly from the deconvolved scalars are filtered explicitly to represent the turbulence-chemistry interaction. However, the approximate deconvolution method (ADM), frequently employed for non-reacting flows, exhibits shortcomings for reacting scalars. This is because ADM does not ensure essential conservation and boundedness conditions. To address this issue, we propose a regularized deconvolution method (RDM) based on an optimization procedure. We conduct a priori and a posteriori analyses to examine RDM as a closure in LES. These investigations are performed in the context of explicit filtering. By showing that RDM is accurate and stable with respect to both the filter width and time, we conclude that the new deconvolution method shows promise in application to combustion LES.

Spectroscopic Ellipsometry of Mucin Layers on an Amphiphilic Diblock Copolymer Surface

Both visible and infrared (IR) spectroscopic ellipsometry have been employed to study the structure of thin layers of bovine submaxillary mucin (BSM) adsorbed on poly(acrylic acid-block-methyl methacrylate) (PAA-b-PMMA) copolymer and poly(methyl methacrylate) (PMMA) surfaces at three pH values (3, 7, and 10). The adsorbed mucin layer on the copolymer surface had the greatest thickness (17 nm) when adsorbed from a mucin solution at a pH of 3. For the first time, IR ellipsometry was used to identify adhesive interactions and conformational changes in mucin/polymer double layers. After applying the regularized method of deconvolution in the analysis, the formation of hydrogen bonds between the carboxyl groups of the BSM and PAA-b-PMMA copolymer in double layers has been found. The IR ellipsometry data, in agreement with the visible ellipsometry analysis, indicate the pH dependence of adhesion of mucin to the copolymer surface. There is an increase in the amount of hydrogen-bonded carboxyl groups in mucin deposited at a pH of 3. There is no evidence that the amide groups of the mucin participate in this bonding. At the lower pH, the IR ellipsometry spectra after deconvolution reveal an increase in the proportion of beta-sheets in the BSM upon adsorption on the copolymer surface, indicating a more unfolded, aggregated structure. The IR ellipsometry data also indicated some changes in the conformational states of the side groups in the copolymer induced by entanglements and bonding interactions with the mucin macromolecules. Deconvolution provides an unprecedented level of information from the IR ellipsometry spectra and yields important insights.

Fourier-wavelet regularized deconvolution (ForWaRD) for lidar systems based on TEA–CO 2 laser

Lidar is an efficient tool for remotely measuring physical quantities or detecting targets. To improve the range resolution in long pulse lidars, such as lidar systems based on TEA–CO 2 lasers, deconvolution methods were used by previous investigators. Deconvolution is a noise sensitive process. In order to avoid noise amplification during the deconvolution process, the Fourier–wavelet regularized deconvolution method is used to deconvolve and denoise the back-scattered lidar signal simultaneously. This method is applied to lidar systems based on the TEA–CO 2 laser and the results are compared to nitrogen tail clipping method. Numerical simulation shows, in comparison to the clipping nitrogen tail technique, by using the ForWaRD method; the range resolution and working distance of the lidar is improved and the clipping tail apparatus is also eliminated.

Spectroscopic manifestation of stretching vibrations of glycosidic linkage in polysaccharides

Manifestation of stretching vibrations of glycosidic linkage in the infrared spectra of polysaccharides (native, microcrystalline, mercerized celluloses, amylose, starches) has been studied using the regularized method of deconvolution. It has been shown that the glycosidic linkage formation in the polysaccharides is characterized by the appearance of new absorption bands in the 1175–1140 cm −1 range as compared to their corresponding monomers. In the 1000−920 cm −1 region differences between the infrared spectra of polysaccharides due to the changes in the glycosidic linkage configuration have been found.

Infrared Ellipsometric Studies of Mixing in a Thin Double Layer of Poly(n-butyl methacrylate)/Poly(vinyl chloride)

Infrared spectroscopic ellipsometry (IRSE), infrared transmission spectroscopy, and the regularized method of deconvolution are applied cooperatively for the analysis of the process of mixing in thin double layers (about 100 nm each) of poly(n-butyl methacrylate) (PnBMA) and poly(vinyl chloride) (PVC) on gold. For the first time thin polymeric double layers in the nanometer range and the annealing induced interactions have been investigated by IRSE. Spectral changes in the ellipsometric spectra of thin films are compared to those observed in the infrared absorbance of 5−10 μm thick films of the same polymers. The stabilization of conformational states of side-chain ester groups has been revealed after the annealing process and cooling to room temperature. The formation of hydrogen-bond-type interactions between the PnBMA carbonyl and PVC methine groups in the annealed double layer is found. With increasing annealing time the PnBMA/PVC sample progressively approaches a mixed homogeneous state.

Regularized Method of Spectral Curve Deconvolution

A regularized method of spectral curve deconvolution is proposed. This method is based on three fundamental principles: the regularized method of solving the convolution equation; the use, instead of the apodization function, of the digital low-pass filter, which permits exact knowledge of its characteristics; and the use of the Fourier transform modulus of the spectrum being treated for obtaining a priori information about the frequency characteristics of the solution and noise, required for determination of the optimum parameters of the regularizing operator. The regularized method of de-convolution permits the acquisition of an approximately stable solution for the deconvolution problem of spectral curves, which moves toward an exact solution with the decrease of the experimental spectrum error. Examples are given of the application of the regularized method of deconvolution to simulated and experimental IR spectra. A conclusion about the expediency of using the given method for resolution enhancement in complex spectra is made.